

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def 	__a     (      A__			:  List[str]			,  A__			:  Optional[Any]			,  A__			:  Optional[int]       ):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE										=   BertConfig.from_json_file(A__       )
    print(F"Building PyTorch model from configuration: {config}"       )
    SCREAMING_SNAKE_CASE										=   BertForPreTraining(A__       )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(A__			,  A__			,  A__       )

    # Save pytorch-model
    print(F"Save PyTorch model to {pytorch_dump_path}"       )
    torch.save(model.state_dict()			,  A__       )


if __name__ == "__main__":
    __A			:					Optional[Any]						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--bert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __A			:					Optional[Any]						=			parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


